3.1.1. Methodology

We calculated a basic community characteristic, the specific richness, to see if patterns could be detected in the study area. The same calculation as for Chapter 1 have been performed for the considered stations.

ASSUMPTION: A higher richness indicates a high status without perturbation.

3.1.2. Application

3.1.3. Ecological Quality Status

3.2.1. Methodology

We calculated basic community characteristics, the total density and biomass of individuals, to see if patterns could be detected in the study area. The same calculations as for Chapter 1 has been performed (with the addition of biomass data) for the considered stations.

3.2.2. Application

3.3.1. Methodology

We calculated basic community characteristics, the Shannon diversity and Pielou evenness, to see if patterns could be detected in the study area. The same calculations as for Chapter 1 has been performed for the considered stations.

ASSUMPTION: A higher diversity indicates a high status without perturbation.

3.3.2. Application

3.3.3. Ecological Quality Status

3.4.1. Methodology

We calculated a basic community characteristic, the taxonomic distinctness, to see if patterns could be detected in the study area. The same calculations as for Chapter 1 has been performed for the considered stations.

3.4.2. Application

3.5.1. Methodology

We studied functional diversity based on these species traits:

• body composition (non calcified tissue, calcareous, calcareous calcium carbonate, calcareous amorphous calcium carbonate, calcareous aragonite, calcareous calcite, calcareous high magnesium calcite, chitinous)
• body size (small, medium, large)
• food diet (filter, surface deposit, subsurface deposit, predator, scavenger, grazer, parasite)
• mobility (sessile, limited, mobile)
• lifestyle (fixed, tubicolous, burrower, crawler, swimmer)

Species were assigned to a trait using a binary code (0: absence of the trait, 1: presence). This allowed to calculate functional richness, divergence and evenness according to Laliberté & Legendre (2010).

3.5.2. Application

3.6.1. Methodology

AMBI (also called biotic coefficient) is an ecological index that is used to detect a perturbation in an ecosystem based on the composition of the communities (Borja et al., 2000). This perturbation is linked with an organic matter increase, according to Pearson and Rosenberg (1978)’s model.

To compute the index, species are classed into five groups in relation to their tolerance to this perturbation:

• group I (GI): vulnerable species
• group II (GII): indifferent species
• group III (GIII): tolerant species
• group IV (GIV): first-order opportunistics
• group V (GV): second-order opportunistics

These groups are based on expert opinion on the physiology of species and experimental studies, but the attribution of a species to a group can be somewhat arbitrary (e.g. based on related phyla information) so it needs to be interpretated carefully.

AMBI is continuous between 0 to 6, and is calculated using this equation:

\[ AMBI = \frac{\sum_{i}^{GI-V} w_{i} . P_{i}}{100} \]

• \(P_{i}\) is the proportion of each group (percentage of the total density of species)
• \(w_{i}\) is the weighting parameter of each group (respectively 0, 1.5, 3, 4.5 and 6)
• \(i\) is the ecological group

ASSUMPTION: Sensitive species are only present in pristine ecosystems, while the dominance of opportunists indicates a perturbed state.

3.6.2. Application

3.7.1. Methodology

M-AMBI is a complementary method that is used to calculate an Ecological Quality Ratio (EQR), a measure of the good environmental status. It is based on a multivariate ordination of the stations using the AMBI index, the species richness and the Shannon diversity. The result gives a value between 0 and 1 after comparison to reference values.

These values are called “references” but this needs to be discussed as this vision is limited. They have been set with the 95 % percentile of the distribution. This is a recommendation by Nicolas Desroy, so that we do not detect an increase of EQR when there is a small perturbation (see work by Pearson & Rosenberg and the Intermediate Disturbance Hypothesis).

This calculation yielded 21 for S and 2.53 for H.

M-AMBI is continuous between 0 and 1, and is calculated using a dedicated software.

ASSUMPTION: A high richness, high diversity and low AMBI index indicate a high status without perturbation.

3.7.2. Application

3.7.3. Ecological Quality Status

No clear tendancy can be discovered here, apart from the fact that the overall status seems to be “High”. Several hypothesises can explain this result:

• the M-AMBI index describes reality well, so that overall perturbation from organic matter is low
• there is a bias in the index due to the species classification in groups, originally suited for European ecosystems
• the assumptions for the reference values are not correct
• the configuration of the bay makes the perturbation small relative to the water volume and bathymetric condition

Further work is needed to determine the individual responses of somes species, along with the use of different methods to understand other perturbations and cumulative impacts.

3.8.1. Methodology

BENTIX is an index based on the same theory as the AMBI, where species are placed in groups based on their tolerance to perturbation (Simboura & Zenetos, 2002). Here also, this perturbation is principally linked to organic matter increase, but two groups only are present:

• GS: species that are sensitive or indifferent to a perturbation (~ AMBI groups I and II)
• GT: species that are tolerant to a perturbation and opportunists (~ AMBI groups III to V)

BENTIX is continuous between 2 and 6 (0 when the habitat is azoic, thus considered highly perturbed), and is calculated using this equation:

\[ BENTIX = \frac{(6 . P_{GS}) + (2 . P_{GT})}{100} \]

• \(P_{GS}\) is the proportion of sensitive species (percentage of the total density of species)
• \(P_{GT}\) is the proportion of tolerant species (percentage of the total density of species)

ASSUMPTION: Sensitive species are only present in pristine ecosystems, while the dominance of opportunists indicates a perturbed state.

3.8.2. Application

3.8.3. Ecological Quality Status

3.9.1. Methodology

BOPA is an index that uses a relative abundance ratio of species in a community to infer a state of perturbation. Ratios with many species have been tested, and opportunistic polychaetes and amphipods have been selected to be the most pertinent (originally to detect effects of an oil-spill on soft-bottom communities, e.g. from the Sea Empress or the Amoco Cadiz). It has been updated from its original form in 2000.

BOPA is continuous between 0 and \(log_{10}(2)\) (~ 0.3), and is calculated using this equation:

\[ BOPA = \left( \frac{f_{P}}{f_{A} + 1} + 1 \right) \]

• \(f_{P}\) is the relative frequency of opportunistic polychaetes (abundance / total density)
• \(f_{A}\) is the relative frequency of amphipods (abundance / total density)

We considered AMBI groups GIII to GV for polychaetes and GI for amphipods (without Jassa genera).

ASSUMPTION: Dominance of amphipods characterizes pristine ecosystems, while a dominance of opportunistic polychaetes indicates a perturbed state.

3.9.2. Application

These are the polychaetes and amphipods present in our species list (including the confidence score used during group classification).

3.9.3. Ecological Quality Status

To use the EQR classification, we used the conversion method from Dauvin & Ruellet (2007).